Literature DB >> 16755527

HIV-1 viral fitness estimation using exchangeable on subsets priors and prior model selection.

Christina M R Kitchen1, Robert E Weiss, Gang Liu, Terri Wrin.   

Abstract

The phenotype-genotype problem is a fundamental problem of biology where an organism's genotype (genetic information) predicts its phenotype (observable characteristic). Viral fitness, defined as the reproductive capacity of a virus compared to a standard, is a continuous phenotype. We construct models to predict viral fitness as a function of mutation away from the standard wildtype virus. Data of this nature are difficult to analyse because there are potentially many more parameters than observations. We treat this issue as a regression problem using a prior with both a shrinkage component and a variable selection component. The key to practical implementation of the model is the prior specification for the regression coefficients. We use results from the scientific literature to construct several informative exchangeable within subsets priors (ESP). We use prior model selection (PMS) to select among our priors. Two novel graphics present results from five models each with 71 predictors. Copyright (c) 2006 John Wiley & Sons, Ltd.

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Year:  2007        PMID: 16755527     DOI: 10.1002/sim.2595

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  4 in total

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Journal:  Am J Med Genet B Neuropsychiatr Genet       Date:  2008-09-05       Impact factor: 3.568

2.  In Vivo validation of a bioinformatics based tool to identify reduced replication capacity in HIV-1.

Authors:  Christina M R Kitchen; Paul Krogstad; Scott G Kitchen
Journal:  Open Med Inform J       Date:  2010-12-03

3.  Bayesian variable selection based on clinical relevance weights in small sample studies-Application to colon cancer.

Authors:  Sandrine Boulet; Moreno Ursino; Peter Thall; Anne-Sophie Jannot; Sarah Zohar
Journal:  Stat Med       Date:  2019-01-22       Impact factor: 2.373

4.  An integrative framework for Bayesian variable selection with informative priors for identifying genes and pathways.

Authors:  Bin Peng; Dianwen Zhu; Bradley P Ander; Xiaoshuai Zhang; Fuzhong Xue; Frank R Sharp; Xiaowei Yang
Journal:  PLoS One       Date:  2013-07-03       Impact factor: 3.240

  4 in total

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